ITG turbulence in gyrokinetic simulations of high collisionality spherical tokamak plasmas
Bibliographic record
Abstract
Abstract This paper presents a first detailed gyrokinetic analysis with the goal of understanding the dominant turbulent transport mechanisms and identifying the micro-instabilities present in small-aspect-ratio plasmas in the PI3 device, developed as magnetized target fusion targets. These plasmas are characterized by low temperatures and high collisionality compared to standard tokamaks. Linear and ion-scale nonlinear gyrokinetic flux tube simulations are performed at radial positions r / a = 0.60 , 0.65, 0.70, and 0.75 using the gyrokinetic code CGYRO (Candy et al 2016 J. Comput. Phys. 324 73). Linear stability analysis finds that ion temperature gradient (ITG) modes dominate at ion scales, while electron-temperature gradient modes dominate at electron scales. Trapped electron modes (TEMs) remain stable due to high collisionality. At very low k y ρ s , microtearing modes (MTMs) are linearly unstable at all radial locations. In the nonlinear regime, turbulence is driven primarily by ITG modes, which dominate both ion and electron energy fluxes. Interestingly, although MTMs are linearly unstable, they are suppressed in the nonlinear phase, except for a small, negative magnetic flutter contribution at the outer radius ( r / a = 0.75 ) that slightly reduces the total electron energy flux. The sensitivity of these instabilities to key plasma parameters is investigated. High collisionality significantly reduces nonlinear turbulent fluxes, and lowering collisionality results in a stronger flux increase than an equivalent increase in plasma beta does. Increasing the T i / T e ratio in the linear analysis stabilizes ITG modes, while simultaneously destabilizing long-wavelength MTMs. Finally, turbulent energy fluxes are compared to neoclassical transport values simulated using NEO (Belli et al 2008 Plasma Phys. Control. Fusion 50 095010), showing transport is anomalous at all radii.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".